GridsearchCV调参

最新推荐文章于 2023-09-26 21:08:30 发布

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最新推荐文章于 2023-09-26 21:08:30 发布

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CC 4.0 BY-SA版权

文章标签：数据结构与算法人工智能

原文链接：http://www.cnblogs.com/fionacai/p/7125249.html

本文详细介绍了使用GridSearchCV进行参数调优时，scoring参数的具体选项及其含义。包括多种评估指标如accuracy、f1_score等，并针对分类、回归及聚类任务提供了不同的评分函数。

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在利用gridseachcv进行调参时，其中关于scoring可以填的参数在SKlearn中没有写清楚，就自己找了下，具体如下：

parameters = {'eps':[0.3,0.4,0.5,0.6], 'min_samples':[20,30,40]}
db = DBSCAN(metric='cosine', algorithm='brute').fit(xx)
grid = GridSearchCV(db, parameters, cv=5, scoring='adjusted_rand_score')

Scoring	Function	Comment
Classification
‘accuracy’	`metrics.accuracy_score`
‘average_precision’	`metrics.average_precision_score`
‘f1’	`metrics.f1_score`	for binary targets
‘f1_micro’	`metrics.f1_score`	micro-averaged
‘f1_macro’	`metrics.f1_score`	macro-averaged
‘f1_weighted’	`metrics.f1_score`	weighted average
‘f1_samples’	`metrics.f1_score`	by multilabel sample
‘neg_log_loss’	`metrics.log_loss`	requires `predict_proba` support
‘precision’ etc.	`metrics.precision_score`	suffixes apply as with ‘f1’
‘recall’ etc.	`metrics.recall_score`	suffixes apply as with ‘f1’
‘roc_auc’	`metrics.roc_auc_score`
Clustering
‘adjusted_rand_score’	`metrics.adjusted_rand_score`
Regression
‘neg_mean_absolute_error’	`metrics.mean_absolute_error`
‘neg_mean_squared_error’	`metrics.mean_squared_error`
‘neg_median_absolute_error’	`metrics.median_absolute_error`
‘r2’	`metrics.r2_score`